Word embeddings are known to encapsulate semantic similarity and have become the preferred representation solution for NLP models. However, they fail to identify the type of semantic relationship, which -in some applications -might be crucial. This paper adapts an existing solution for enhancing word embedding representations such as to better separate between synonyms and antonyms in an intent detection task applied to a Romanian home assistant scenario. Accounting for the morphological richness of the Romanian language, our method proposes an additional augmentation step, in order to generate conjugated pairs of antonym and synonym verbs. The generated pairs are run through the counterfitting step (inspired from literature), for which we propose a justified improvement for one of the hyperparameters. The evaluations performed on the home assistant scenario have shown that the pre-processing step has an essential role in reducing opposing intent errors in the classification model (by almost two thirds).